Methods for developing and implementing large language models in healthcare: challenges and prospects in Russia

Large language models (LLMs) are transforming healthcare by enabling the analysis of clinical texts, supporting diagnostics, and facilitating decision-making. This systematic review examines the evolution of LLMs from recurrent neural networks (RNNs) to transformer-based and multimodal architectures...

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Vydáno v:Discrete and continuous models and applied computational science Ročník 33; číslo 3; s. 327 - 344
Hlavní autoři: Shchetinin, Eugeny Yu, Velieva, Tatyana R., Yurgina, Lyubov A., Demidova, Anastasia V., Sevastianov, Leonid A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Peoples’ Friendship University of Russia (RUDN University) 15.10.2025
Témata:
clinical text analysis
deep learning
healthcare
interpretability
large language models
radiology report generation
russian healthcare
ISSN:2658-4670, 2658-7149
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Shrnutí:Large language models (LLMs) are transforming healthcare by enabling the analysis of clinical texts, supporting diagnostics, and facilitating decision-making. This systematic review examines the evolution of LLMs from recurrent neural networks (RNNs) to transformer-based and multimodal architectures (e.g., BioBERT, MedPaLM), with a focus on their application in medical practice and challenges in Russia. Based on 40 peer-reviewed articles from Scopus, PubMed, and other reliable sources (2019-2025), LLMs demonstrate high performance (e.g., Med-PaLM: F1-score 0.88 for binary pneumonia classification on MIMIC-CXR; Flamingo-CXR: 77.7% preference for in/outpatient X-ray re-ports). However, limitations include data scarcity, interpretability challenges, and privacy concerns. An adaptation of the Mixture of Experts (MoE) architecture for rare disease diagnostics and automated radiology report generation achieved promising results on synthetic datasets. Challenges in Russia include limited annotated data and compliance with Federal Law No. 152-FZ. LLMs enhance clinical workflows by automating routine tasks, such as report generation and patient triage, with advanced models like KARGEN improving radiology report quality. Russia’s focus on AI-driven healthcare aligns with global trends, yet linguistic and infrastructural barriers necessitate tailored solutions. Developing robust validation frameworks for LLMs will ensure their reliability in diverse clinical scenarios. Collaborative efforts with international AI research communities could accelerate Russia’s adoption of advanced medical AI technologies, particularly in radiology automation. Prospects involve integrating LLMs with healthcare systems and developing specialized models for Russian medical contexts. This study provides a foundation for advancing AI-driven healthcare in Russia.
ISSN:2658-4670
2658-7149
DOI:10.22363/2658-4670-2025-33-3-327-344